Falkner–skan flow with stream-wise pressure gradient and transfer of mass over a dynamic wall
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Date
2021-11
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Abstract
In this work, an important model in fluid dynamics is analyzed by a new hybrid neurocom-puting algorithm. We have considered the Falkner–Skan (FS) with the stream-wise pressure gradient transfer of mass over a dynamic wall. To analyze the boundary flow of the FS model, we have utilized the global search characteristic of a recently developed heuristic, the Sine Cosine Algorithm (SCA), and the local search characteristic of Sequential Quadratic Programming (SQP). Artificial neural network (ANN) architecture is utilized to construct a series solution of the mathematical model. We have called our technique the ANN-SCA-SQP algorithm. The dynamic of the FS system is observed by varying stream-wise pressure gradient mass transfer and dynamic wall. To validate the effectiveness of ANN-SCA-SQP algorithm, our solutions are compared with state-of-the-art reference solutions. We have repeated a hundred experiments to establish the robustness of our approach. Our experimental outcome validates the superiority of the ANN-SCA-SQP algorithm.
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Keywords
Artificial neural networks, Computational fluid dynam-ics, Computational science, Differential equations, Falkner–Skan system, Fluid dynamics, Hybrid computing, Mass transfer, Numerical methods, Sequential quadratic programming, Sine-cosine algorithm
Citation
Khan, M. F., Sulaiman, M., Tavera Romero, C. A., & Alkhathlan, A. (2021b). Falkner–skan flow with stream-wise pressure gradient and transfer of mass over a dynamic wall. Entropy, 23(11). https://doi.org/10.3390/e23111448